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2016•Unpublished venueRequires access

Finding and Ranking Semantic Web Sites

Zhang Xiang, Weiyi Ge, Yuzhong Qu

Open publisher page 1 citations

Abstract

Abstract: With the rapid growth of online RDF data, emerging semantic search engines facilitate user’s searching of RDF (resource description framework) data. It is an open question to all semantic search engines how to find sites containing semantic Web information resources automatically and collect them efficiently. Firstly, the paper introduces a Linking Model of the Semantic Web Sites. The model characterizes the relations among Semantic Web Sites, Semantic Web Information Resources, RDF Models and Semantic Web Entities. This paper discusses the ownerships of Semantic Web Entities based on this model. It also defines a Site Dependency Graph in virtue of the model, and presents a set of ranking algorithms for Semantic Web Sites. Primary tests of these algorithms have been performed in a real-world semantic search engine. Experimental results show that this approach is effective in finding and ranking Semantic Web Sites.

About this research paper

What this paper is about

Abstract: With the rapid growth of online RDF data, emerging semantic search engines facilitate user’s searching of RDF (resource description framework) data. It is an open question to all semantic search engines how to find sites containing semantic Web information resources automatically and collect them efficiently. Firstly, the paper introduces a Linking Model of the Semantic Web Sites. The model characterizes the relations among Semantic Web Sites, Semantic Web Information Resources, RDF Models and Semantic Web Entities. This paper discusses the ownerships of Semantic Web Entities based on this model. It also defines a Site Dependency Graph in virtue of the model, and presents a set of ranking algorithms for Semantic Web Sites. Primary tests of these algorithms have been performed in a real-world semantic search engine. Experimental results show that this approach is effective in finding and ranking Semantic Web Sites.

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Available abstract

Abstract: With the rapid growth of online RDF data, emerging semantic search engines facilitate user’s searching of RDF (resource description framework) data. It is an open question to all semantic search engines how to find sites containing semantic Web information resources automatically and collect them efficiently. Firstly, the paper introduces a Linking Model of the Semantic Web Sites. The model characterizes the relations among Semantic Web Sites, Semantic Web Information Resources, RDF Models and Semantic Web Entities. This paper discusses the ownerships of Semantic Web Entities based on this model. It also defines a Site Dependency Graph in virtue of the model, and presents a set of ranking algorithms for Semantic Web Sites. Primary tests of these algorithms have been performed in a real-world semantic search engine. Experimental results show that this approach is effective in finding and ranking Semantic Web Sites.

Key concepts: Computer science, Social Semantic Web, Semantic analytics, Semantic Web Stack, Semantic Web, Information retrieval, Data Web, Semantic grid

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